The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification

Autores
Palopoli, Nicolás; Iserte, Javier Alonso; Chemes, Lucia Beatriz; Marino Buslje, Cristina; Parisi, Gustavo Daniel; Gibson, Toby James; Davey, N.E.
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Modern biology produces data at a staggering rate. Yet, much of these biological data is still isolated in the text, figures, tables and supplementary materials of articles. As a result, biological information created at great expense is significantly underutilised. The protein motif biology field does not have sufficient resources to curate the corpus of motif-related literature and, to date, only a fraction of the available articles have been curated. In this study, we develop a set of tools and a web resource, 'articles.ELM', to rapidly identify the motif literature articles pertinent to a researcher's interest. At the core of the resource is a manually curated set of about 8000 motif-related articles. These articles are automatically annotated with a range of relevant biological data allowing in-depth search functionality. Machine-learning article classification is used to group articles based on their similarity to manually curated motif classes in the Eukaryotic Linear Motif resource. Articles can also be manually classified within the resource. The 'articles.ELM' resource permits the rapid and accurate discovery of relevant motif articles thereby improving the visibility of motif literature and simplifying the recovery of valuable biological insights sequestered within scientific articles. Consequently, this web resource removes a critical bottleneck in scientific productivity for the motif biology field.
Fil: Palopoli, Nicolás. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Iserte, Javier Alonso. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Chemes, Lucia Beatriz. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Marino Buslje, Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Parisi, Gustavo Daniel. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gibson, Toby James. Ruprecht Karls Universitat Heidelberg; Alemania
Fil: Davey, N.E.. The Institute of Cancer Research; Reino Unido
Materia
LINEAR MOTIF
TEXT MINING
DATABASE
DISCOVERY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/137873
Acceder
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network_name_str CONICET Digital (CONICET)
spelling The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classificationPalopoli, NicolásIserte, Javier AlonsoChemes, Lucia BeatrizMarino Buslje, CristinaParisi, Gustavo DanielGibson, Toby JamesDavey, N.E.LINEAR MOTIFTEXT MININGDATABASEDISCOVERYhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Modern biology produces data at a staggering rate. Yet, much of these biological data is still isolated in the text, figures, tables and supplementary materials of articles. As a result, biological information created at great expense is significantly underutilised. The protein motif biology field does not have sufficient resources to curate the corpus of motif-related literature and, to date, only a fraction of the available articles have been curated. In this study, we develop a set of tools and a web resource, 'articles.ELM', to rapidly identify the motif literature articles pertinent to a researcher's interest. At the core of the resource is a manually curated set of about 8000 motif-related articles. These articles are automatically annotated with a range of relevant biological data allowing in-depth search functionality. Machine-learning article classification is used to group articles based on their similarity to manually curated motif classes in the Eukaryotic Linear Motif resource. Articles can also be manually classified within the resource. The 'articles.ELM' resource permits the rapid and accurate discovery of relevant motif articles thereby improving the visibility of motif literature and simplifying the recovery of valuable biological insights sequestered within scientific articles. Consequently, this web resource removes a critical bottleneck in scientific productivity for the motif biology field.Fil: Palopoli, Nicolás. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Iserte, Javier Alonso. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Chemes, Lucia Beatriz. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Marino Buslje, Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Parisi, Gustavo Daniel. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gibson, Toby James. Ruprecht Karls Universitat Heidelberg; AlemaniaFil: Davey, N.E.. The Institute of Cancer Research; Reino UnidoOxford University Press2020-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/137873Palopoli, Nicolás; Iserte, Javier Alonso; Chemes, Lucia Beatriz; Marino Buslje, Cristina; Parisi, Gustavo Daniel; et al.; The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification; Oxford University Press; Database; 2020; 1-2020; 1-101758-04631758-0463CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/database/article/doi/10.1093/database/baaa040/5850858info:eu-repo/semantics/altIdentifier/doi/10.1093/database/baaa040info:eu-repo/semantics/altIdentifier/url/http://slim.icr.ac.uk/articles/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:39:03Zoai:ri.conicet.gov.ar:11336/137873instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:39:03.7CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification
title The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification
spellingShingle The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification
Palopoli, Nicolás
LINEAR MOTIF
TEXT MINING
DATABASE
DISCOVERY
title_short The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification
title_full The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification
title_fullStr The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification
title_full_unstemmed The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification
title_sort The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification
dc.creator.none.fl_str_mv Palopoli, Nicolás
Iserte, Javier Alonso
Chemes, Lucia Beatriz
Marino Buslje, Cristina
Parisi, Gustavo Daniel
Gibson, Toby James
Davey, N.E.
author Palopoli, Nicolás
author_facet Palopoli, Nicolás
Iserte, Javier Alonso
Chemes, Lucia Beatriz
Marino Buslje, Cristina
Parisi, Gustavo Daniel
Gibson, Toby James
Davey, N.E.
author_role author
author2 Iserte, Javier Alonso
Chemes, Lucia Beatriz
Marino Buslje, Cristina
Parisi, Gustavo Daniel
Gibson, Toby James
Davey, N.E.
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv LINEAR MOTIF
TEXT MINING
DATABASE
DISCOVERY
topic LINEAR MOTIF
TEXT MINING
DATABASE
DISCOVERY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Modern biology produces data at a staggering rate. Yet, much of these biological data is still isolated in the text, figures, tables and supplementary materials of articles. As a result, biological information created at great expense is significantly underutilised. The protein motif biology field does not have sufficient resources to curate the corpus of motif-related literature and, to date, only a fraction of the available articles have been curated. In this study, we develop a set of tools and a web resource, 'articles.ELM', to rapidly identify the motif literature articles pertinent to a researcher's interest. At the core of the resource is a manually curated set of about 8000 motif-related articles. These articles are automatically annotated with a range of relevant biological data allowing in-depth search functionality. Machine-learning article classification is used to group articles based on their similarity to manually curated motif classes in the Eukaryotic Linear Motif resource. Articles can also be manually classified within the resource. The 'articles.ELM' resource permits the rapid and accurate discovery of relevant motif articles thereby improving the visibility of motif literature and simplifying the recovery of valuable biological insights sequestered within scientific articles. Consequently, this web resource removes a critical bottleneck in scientific productivity for the motif biology field.
Fil: Palopoli, Nicolás. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Iserte, Javier Alonso. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Chemes, Lucia Beatriz. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Marino Buslje, Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Parisi, Gustavo Daniel. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gibson, Toby James. Ruprecht Karls Universitat Heidelberg; Alemania
Fil: Davey, N.E.. The Institute of Cancer Research; Reino Unido
description Modern biology produces data at a staggering rate. Yet, much of these biological data is still isolated in the text, figures, tables and supplementary materials of articles. As a result, biological information created at great expense is significantly underutilised. The protein motif biology field does not have sufficient resources to curate the corpus of motif-related literature and, to date, only a fraction of the available articles have been curated. In this study, we develop a set of tools and a web resource, 'articles.ELM', to rapidly identify the motif literature articles pertinent to a researcher's interest. At the core of the resource is a manually curated set of about 8000 motif-related articles. These articles are automatically annotated with a range of relevant biological data allowing in-depth search functionality. Machine-learning article classification is used to group articles based on their similarity to manually curated motif classes in the Eukaryotic Linear Motif resource. Articles can also be manually classified within the resource. The 'articles.ELM' resource permits the rapid and accurate discovery of relevant motif articles thereby improving the visibility of motif literature and simplifying the recovery of valuable biological insights sequestered within scientific articles. Consequently, this web resource removes a critical bottleneck in scientific productivity for the motif biology field.
publishDate 2020
dc.date.none.fl_str_mv 2020-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/137873
Palopoli, Nicolás; Iserte, Javier Alonso; Chemes, Lucia Beatriz; Marino Buslje, Cristina; Parisi, Gustavo Daniel; et al.; The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification; Oxford University Press; Database; 2020; 1-2020; 1-10
1758-0463
1758-0463
CONICET Digital
CONICET
url http://hdl.handle.net/11336/137873
identifier_str_mv Palopoli, Nicolás; Iserte, Javier Alonso; Chemes, Lucia Beatriz; Marino Buslje, Cristina; Parisi, Gustavo Daniel; et al.; The articles.ELM resource: Simplifying access to protein linear motif literature by annotation, text-mining and classification; Oxford University Press; Database; 2020; 1-2020; 1-10
1758-0463
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/database/article/doi/10.1093/database/baaa040/5850858
info:eu-repo/semantics/altIdentifier/doi/10.1093/database/baaa040
info:eu-repo/semantics/altIdentifier/url/http://slim.icr.ac.uk/articles/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.069144

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